Bordering non-citizenship assemblage through migrant legibility: a conceptual framework for tracing hidden forms of legal and bureaucratic violence
Bibliographic record
Abstract
In this paper, we conceptualise migrant legibility as a bordering practice where migrants seeking to maintain status or transition to permanent residency in Canada must negotiate the dynamic milieu of: 1) laws and regulations governing immigrant inclusion; 2) bureaucratic processes for verifying eligibility and admissibility; 3) informal social networks which can expand or restrict access to information and resources. Using two case studies from empirical research with migrants in Canada, we attend to the legal, bureaucratic, and social processes through which migrants must prove their humanity (i.e., biopolitical life) in the context of unpredictable, heterogeneous, multi-scalar, and often hidden forms of legal and bureaucratic violence. Through theorising the legal and bureaucratic violence of legibility, this paper illustrates the historical, political, and economic conditions through which migrant illegality and patterns of imperial/colonial/racial/gendered ordering operate in tandem with neoliberal multicultural constructions of equality and inclusion of autonomous and self-sufficient individuals.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.007 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.006 | 0.003 |
| Science and technology studies | 0.014 | 0.110 |
| Scholarly communication | 0.011 | 0.010 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".